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, transcriptional recording (Record-seq), and related technologies. Develop and apply statistical methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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inference, analysis of high-dimensional and -omics data, Bayesian methods, and clinical trials, with active collaborations in cancer, aging, HIV, and the analysis of large-scale health data. The School
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principles and analytic methods relevant in health services research Advanced knowledge of statistical computing and/or Bayesian inference Advanced programming skills in a common statistical software package
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Tenure-Track Biostatistics Faculty - (25002815) Description EPIDEMIOLOGY AND BIOSTATISTICS Tenure-Track Biostatistics Faculty We invite applications for tenure-track faculty in biostatistics to join
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). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials